Senior Data Engineer

Job Title: Senior Data Engineer (Data Quality & Automation Focus)

Location: Leawood, KS

Job Type: Contract

Job Description

We are seeking a Senior Data Engineer with strong experience in building and maintaining modern data platforms, and a solid background in data quality, validation, and automation. This role is ideal for a hands-on data engineer who has worked closely with QE or testing teams and has personally implemented data testing frameworks, profiling, and reconciliation for large-scale data pipelines.

The candidate will play a key role in designing, developing, and validating Snowflake-based data platforms and ETL/ELT pipelines, ensuring data accuracy, reliability, performance, and governance across the data ecosystem.

Key Responsibilities

• Design, develop, and optimize ETL/ELT data pipelines for large-scale data platforms.
• Build and maintain Snowflake and SQL Server based data warehouses.
• Perform advanced SQL-based data profiling, reconciliation, and validation to ensure data accuracy and integrity.
• Implement data quality checks, automation frameworks, and shift-left testing strategies within the data pipeline.
• Develop Python-based automation for data validation, testing, and monitoring.
• Integrate data quality and validation processes into CI/CD pipelines.
• Leverage tools such as Soda and Coalesce to enforce data quality, observability, and governance.
• Collaborate closely with Data Engineers, Architects, Analytics teams, and QE members to define best practices.
• Identify, analyze, and resolve complex data issues, performance bottlenecks, and pipeline failures.
• Support Agile delivery by participating in sprint planning, code reviews, and continuous improvement initiatives.
• Mentor junior engineers and promote strong data engineering and quality engineering practices.

Required Skills & Experience

• 10 15 years of experience in Data Engineering, with exposure to data quality or data testing.
• Strong hands-on experience with Snowflake and SQL Server.
• Deep knowledge of Data Warehousing concepts, dimensional modeling, and data architecture.
• Extensive experience with ETL/ELT pipelines and large-scale data processing.
• Expert-level SQL for data profiling, validation, reconciliation, and performance tuning.
• Strong Python skills for automation, scripting, and data validation.
• Experience implementing data quality frameworks and automated data testing.
• Hands-on experience with Soda, Coalesce, or similar modern data stack tools.
• Solid understanding of QE processes, test strategies, and shift-left testing concepts.
• Experience working in Agile/Scrum environments.
• Strong knowledge of CI/CD pipelines and integrating data workflows into automated deployments.
• Excellent analytical, problem-solving, and communication skills.